Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Divide-and-Conquer Reinforcement Learning
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1711.09874.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T21:09:35.599124Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-04T15:29:55.609297Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 40021a4c-6331-4dea-b781-0e0e78e441c1 · inbound
Prescribe-then-Select: Adaptive Policy Selection for Contextual Stochastic Optimization cites this paper.
Prescribe-then-Select: Adaptive Policy Selection for Contextual Stochastic Optimization Divide-and-Conquer Reinforcement Learning
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T21:09:35.599124Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 46cde392-cc1f-414e-8b1b-7e64e3dc2f2e · inbound
Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling cites this paper.
Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling Divide-and-Conquer Reinforcement Learning
Reference 241
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-15T03:08:59.526177Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 4a734384-1404-4f7f-8e03-ae4f26cc5bcd · inbound
Mesh-RL: Coupled subgrid reinforcement learning cites this paper.
Mesh-RL: Coupled subgrid reinforcement learning Divide-and-Conquer Reinforcement Learning
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-07-04T15:29:55.611156Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.